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Interns preferred feedback from the direct observation sessions. VPs and faculty also rated the experience highly. Inter-rater reliability was good for the respiratory exam, but poor for the cardiovascular and neurologic exams. Conclusions Direct observation of trainees provides evidence about PE skill that cannot be obtained via simulation. Clinician educators' ability to provide reliable PE assessment may depend on the portion of the PE being assessed. Our experience highlights the need for ongoing training of clinician educators in direct observation, standard setting, and assessment protocols. This assessment can inform summative or formative assessments of physical exam skill in graduate medical education.Background Total haemoglobin (Hb) concentration in blood belongs to the most requested measurands, and the HiCN method (hemiglobincyanide) is accepted as a reference. Although the reaction principle is clearly characterised, measurement conditions and settings are not consistently defined, some of them influencing the results. An improvement of standardisation is the object. click here Methods After method optimization, measurement results between different calibration laboratories (CL) were compared with each other and also with results of the National Metrology Institute of Germany (PTB), with target values of certified reference material, within the RELA scheme, and to >1500 results from routine laboratories. Results Overall deviations between three CLs were ≤0.5% (n = 24 samples) in a measurement range of 20 g/L to 300 g/L. A CV of 0.4% was determined in pooled blood (1 year long-term imprecision, 99.0%-101.1% recovery of the mean). For selected measurements (n = 4 samples) the PTB participated without significant differences to three CLs, and no significant differences were observed comparing CLs to certified values of reference materials. The expanded measurement uncertainty (probability 95%) was estimated as 1.1%. Conclusions A reference measuring system, comprising measuring instruments and other devices, including reagents and supply, to generate reference measurement values for total Hb concentration of high accuracy and low measurement uncertainty is presented. Measurement parameters are investigated and defined. The reference measuring system is ready to offer service to EQA providers and to the IVD industry for certifying control materials or calibrators.Deep convolutional neural networks (CNNs) have demonstrated impressive performance on many visual tasks. Recently, they became useful models for the visual system in neuroscience. However, it is still not clear what is learned by CNNs in terms of neuronal circuits. When a deep CNN with many layers is used for the visual system, it is not easy to compare the structure components of CNNs with possible neuroscience underpinnings due to highly complex circuits from the retina to the higher visual cortex. Here, we address this issue by focusing on single retinal ganglion cells with biophysical models and recording data from animals. By training CNNs with white noise images to predict neuronal responses, we found that fine structures of the retinal receptive field can be revealed. Specifically, convolutional filters learned are resembling biological components of the retinal circuit. This suggests that a CNN learning from one single retinal cell reveals a minimal neural network carried out in this cell. Furthermore, when CNNs learned from different cells are transferred between cells, there is a diversity of transfer learning performance, which indicates that CNNs are cell specific. Moreover, when CNNs are transferred between different types of input images, here white noise versus natural images, transfer learning shows a good performance, which implies that CNNs indeed capture the full computational ability of a single retinal cell for different inputs. Taken together, these results suggest that CNNs could be used to reveal structure components of neuronal circuits, and provide a powerful model for neural system identification.Multimodal optimization problems have multiple satisfactory solutions to identify. Most of the existing works conduct the search based on the information of the current population, which can be inefficient. This article proposes a probabilistic niching evolutionary computation framework that guides the future search based on more sufficient historical information, in order to locate diverse and high-quality solutions. A binary space partition tree is built to structurally organize the space visiting information. Based on the tree, a probabilistic niching strategy is defined to reinforce exploration and exploitation by making full use of the structural historical information. The proposed framework is universal for incorporating various baseline niching algorithms. In this article, we integrate the proposed framework with two niching algorithms 1) a distance-based differential evolution algorithm and 2) a topology-based particle swarm optimization algorithm. The two new algorithms are evaluated on 20 multimodal optimization test functions. The experimental results show that the proposed framework helps the algorithms obtain competitive performance. They outperform a number of state-of-the-art niching algorithms on most of the test functions.Object instance segmentation is one of the most fundamental but challenging tasks in computer vision, and it requires the pixel-level image understanding. Most existing approaches address this problem by adding a mask prediction branch to a two-stage object detector with the region proposal network (RPN). Although producing good segmentation results, the efficiency of these two-stage approaches is far from satisfactory, restricting their applicability in practice. In this article, we propose a one-stage framework, single-pixel reconstruction net (SPRNet), which performs efficient instance segmentation by introducing a single-pixel reconstruction (SPR) branch to off-the-shelf one-stage detectors. The added SPR branch reconstructs the pixel-level mask from every single pixel in the convolution feature map directly. Using the same ResNet-50 backbone, SPRNet achieves comparable mask AP with Mask R-CNN at a higher inference speed and gains all-round improvements on box AP at every scale compared with RetinaNet.